Statistics for Data Analytics

A statistics course built for working Data Analysts, not a general statistics degree. Every lesson passes one test: would a working analyst actually use this on the job. Four modules take you from descriptive statistics and probability through hypothesis testing to regression, all taught on one continuous fictional retailer's data so techniques build on data you already recognize. Closes with two full end-to-end portfolio projects, each ending in a written business recommendation. No unnecessary mathematical depth, no career filler, every number in every lesson verified against real data before it's written down.

Rutvik Acharya

Principal Data Scientist

Atlassian

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What You'll Learn

Profile, clean, and describe any dataset honestly before drawing conclusions from it

Apply probability, confidence intervals, and hypothesis testing to real business questions

Run and interpret t-tests, ANOVA, chi-square, and regression without misusing them

Choose the right statistical test for a given question, every time, using a practical decision framework

Who Should Attend

Data Analysts who know spreadsheets or basic SQL but want to reason correctly with statistics

Aspiring analysts preparing for interviews that test statistical judgment, not just formulas

Anyone who has taken a general statistics course and found it too academic to actually use at work

Analysts who want a portfolio project demonstrating a full analysis-to-recommendation workflow

CERTIFICATION

Certificate of Completion

5 Modules

10 Hours

30 Lessons

30 Challenges

Language: English

FAQ

FREQUENTLY ASKED QUESTIONS

So techniques build on data you already recognize. By the time you reach regression in Module 4, you already understand the customer base, the channels, and the loyalty tiers from Module 1 — the focus stays on the statistics, not on re-learning a new dataset every lesson.

Through a real example where a small number of high-value customers pull the mean well above the median. You learn to check skew first, then choose the measure that actually represents typical for that specific distribution, rather than defaulting to the mean out of habit.

Not as an abstract warning. The course builds a dataset where a marketing variable appears to predict spend, then shows that a third variable (tenure) explains both — a real, working example of a confounder, not just a rule to memorize.

Simple and multiple linear regression, reading coefficients correctly (including what holding other variables constant really means), full diagnostics for checking whether a model can be trusted, and logistic regression for binary outcomes like churn — enough to build and validate a real model, not just run one.

Both. The hypothesis testing module covers how testing gets misused in practice — one-tailed versus two-tailed tests, the difference between statistical and practical significance, and what happens when you run many tests and don't correct for it.

Through natural-frequency reasoning first — working with actual counts of people out of 10,000, not conditional probability notation — using a fraud-screening example. The formula comes after the intuition, not before it.

The first combines profiling, ANOVA, correlation, and regression to test what really predicts customer spend — and finds that satisfaction score isn't one of the real drivers. The second is a full A/B test analysis, from a power calculation through to a device-specific ship recommendation with an estimated revenue impact.

It goes further — ANOVA tells you groups differ, but not which ones. The course pairs it with Tukey's HSD so you can identify the specific pairs of groups driving the difference, which is what a real business question usually needs.

Through a decision framework based on what kind of variable you're actually testing — continuous outcomes call for t-tests or ANOVA, categorical outcomes call for chi-square — reinforced with a reference lesson at the end of the course you can return to whenever you're unsure which test applies.

Yes — the hypothesis testing module includes a case where a large sample size makes a tiny, practically meaningless difference come back statistically significant, specifically to teach the difference between the two.

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